Margin and domain integrated classification

Multi-category classification is an ongoing research topic with numerous applications. In this paper, a novel approach called margin and domain integrated classifier (MDIC) is addressed. It handles multi-class problems as a combination of several target classes plus outliers. The basic idea behind the proposed approach is that target classes possess structured characteristics while outliers scatter around in the feature space. In our approach the domain description and large-margin discrimination are adjustable and therefore higher classification accuracy leads to better performance. The properties of MDIC are analyzed and the performance comparisons using synthetic and real data are presented.